This paper will define how statistics are used in my place of work. I will provide example of descriptive statistics and inferential statistics that are used in my workplace. I will provide examples of how the four levels of measurements are used in my workplace. I will then explain the advantages of accurate interpretation of information to improve decision making in my workplace. Statistics is important in health care at many different levels such as quality measures, patient satisfaction and revenue. Without statistics, you cannot improve quality measures, patient satisfaction, on manage revenue. Statistics is usually divided into two different categories. Descriptive statistics deals with describing raw data in the form of graphics and sample statistics (Bennett, Briggs, & Triola, 2009). Inferential statistics uses sample data from a population to gather data (Bennett, Briggs, & Triola, 2009). There are many ways descriptive statistics is used in my workplace; one is the use of Press Ganey to evaluate the patient’s experience. Press Ganey is an independent company who mails surveys to patients that was discharged home. Each patient who is selected to be mailed a survey is assigned a code that reflects the unit the patient was discharged from as well as the discharged diagnosis. The survey consists of 20 questions about their impressions of the hospital, the staff, and the physicians. The surveys are than sent back to the company where the data is analyzed and forwarded back to our hospital. The data not only provides the quality of care provided, but also compares our hospital to other hospitals within the Press Ganey database. The data is analyzed by the leadership team and determined what areas need improvement. The drawback of Press Ganey is the amount of returned surveys. An example of inferential statistics used in my workplace is with the stroke population. We are a Primary Stroke Center certified by The Joint Commission. There are standards we need to follow and report to American Heart Association, Get with the Guidelines and The Joint Commission to maintain our certification. The purpose of having a certification is to drive best practice for stroke patients and improve the quality of patient care by reduction variation in clinical processes. There are eight stroke core measures that are abstracted through our stroke quality abstractor. The data is submitted quarterly to The Joint Commission through a secured site. The data is reviewed monthly at with the stroke committee with gap analyzes. Case studies are conducted with any charts that have fallouts with any of the eight reportable standards. Data can be classified by level of measurement, which are nominal, ordinal, interval and ratio. Nominal measurement consists of assigning items to groups or categories but has no meaning other than to label a group. There are pain scales that describe the quality of a symptom, such as a nominal measure of the quality of pain may include categories as aching, dull, sharp, or burning. Ordinal measurement assigns values based on their ranking. An example of ordinal measurement is within the Press Ganey survey. There are four choices to answer the questions, always, sometimes, never. Interval measurements are numerical scales in which intervals have the same interpretation throughout (Bennett, Briggs, and Triola, 2009). An example of interval measurement in our healthcare system is used in the hypothermic protocol for rewarming. There are three categories for rewarming the patient depending on the core body temperature; mild >34ºC; moderate 30ºC - 34ºC; and severe I < 30ºC. Using the interval measurements helps to predict the outcome of the patient. The ratio measurement is an interval scale with the additional property that its zero position indicates the absence of the quantity being measured (Bennett, Briggs, and Triola, 2009). Examples of ratio measurement used in our healthcare system are documentation of height, weight, cardiac output, etc. Height and weight data is abstracted and used in our Bariatric Clinic for research purposes. In health care, it is important to know what to measure and how to manage the numbers. Statistics is important in measuring success or failure. The healthcare organization I work for has established benchmarks and standards of service excellence as this helps to measure the future outcome. Quarterly, we analyze the data of any preventable error, infection control issues, hospital acquired diseases and medication errors. Without this data, we could not improve the quality of care we provide and deliver to our patients. The advantage of accurate interpretation of statistical information in healthcare is to measure our performance outcome, patient outcome and provide continuous quality improvement programs to maximize efficiency.

References
Bennett, J. O., Briggs, W. L., & Triola, M .F. (2009). Statistical reasoning for everyday life (3rd ed.). Boston, MA: Pearson Education.